Temporal Difference Variational Auto-Encoder

To act and plan in complex environments, we posit that agents should have a mental simulator of the world with three characteristics: (a) it should build an abstract state representing the condition of the world; (b) it should form a belief which represents uncertainty on the world; (c) it should go beyond simple step-by-step simulation, and exhibit temporal abstraction. Motivated by the absence of a model satisfying all these requirements, we propose TD-VAE, a generative sequence model that learns representations containing explicit beliefs about states several steps into the future, and that can be rolled out directly without single-step transitions. TD-VAE is trained on pairs of temporally separated time points, using an analogue of temporal difference learning used in reinforcement learning.

Authors

• Karol Gregor
• George Papamakarios
• Frederic Besse
• Lars Buesing
• Theophane Weber

Keywords

• generative models
• variational auto-encoders
• state space models
• temporal difference learning